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Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
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Published on: August 4, 2014

Learning dynamical systems with biochemically informed neural ordinary differential equations.

Luis L Fonseca1, Reinhard C Laubenbacher1, Lucas Böttcher2,1

  • 1Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA.

Biorxiv : the Preprint Server for Biology
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

We introduce biochemically informed neural ordinary differential equations (BINODEs) to model complex biological systems. This framework combines mechanistic structure with neural networks, improving interpretability and flexibility in dynamical systems modeling.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Machine Learning

Background:

  • Ordinary differential equation (ODE) models are crucial for biochemical reactions but struggle with unknown process dynamics.
  • Inferring functional forms of biological processes from data is challenging.

Purpose of the Study:

  • To develop a novel framework, biochemically informed neural ordinary differential equations (BINODEs), for modeling biological dynamical systems.
  • To integrate mechanistic stoichiometric structure with data-driven neural network flexibility.

Main Methods:

  • Proposed BINODEs, a neural-ODE framework representing individual processes using neural networks.
  • Mapped neural network process outputs to state derivatives via a linear layer, akin to a stoichiometric matrix.
  • Incorporated biological side information like sign constraints and monotonicity assumptions.

Main Results:

  • Characterized neural network process approximation properties for standard biochemical rate laws.
  • Demonstrated BINODEs' ability to recover system trajectories and process-level structure.
  • Validated the framework on Monod, Lotka-Volterra, pharmacokinetic, and ultradian endocrine models.

Conclusions:

  • BINODEs offer a powerful compromise between mechanistic interpretability and data-driven modeling flexibility.
  • The framework is suitable for partially known biochemical and biological dynamical systems.
  • BINODEs enhance the modeling of complex biological systems with unknown components.